Dynamic network reorganization underlying neuroplasticity: the deficits-severity-related language network dynamics in patients with left hemispheric gliomas involving language network
Dynamic network reorganization underlying neuroplasticity: the deficits-severity-related language network dynamics in patients with left hemispheric gliomas involving language network
复制标题
DOI：
10.1093/cercor/bhad113
复制
发表时间：
2023-04-01
期刊：
影响因子：
3.7
通讯作者：
Yan，Jing
中科院分区：
文献类型：
--
作者：
Yuan，Binke;Xie，Hui;Yan，Jing
脑网络动力学不仅赋予大脑对各种认知过程的灵活协调，而且在发育、技能学习和脑损伤后具有巨大的神经可塑性潜力。弥漫性和进展性胶质瘤浸润触发了功能代偿的神经可塑性，这是研究神经可塑性背后的网络重组的杰出病理生理学模型。在本研究中，我们采用动态条件相关性来构建框架语言网络，并研究了 83 名涉及语言网络的左半球神经胶质瘤患者（40 名无失语症患者和 43 名失语症患者）的动态重组。我们发现，在健康对照 (HC) 和患者中，静息状态下的语言网络动态聚集成 4 个时间重复状态。观察到 dFC 的语言缺陷-严重程度依赖性拓扑异常。与 HC 相比，那些没有失语症的患者观察到了次优的语言网络动态，而那些失语症患者则观察到了更严重的网络破坏。基于机器学习的 dFC 语言学预测分析表明，4 个州的 dFC 显着预测了个体患者的语言分数。这些发现阐明了我们对神经胶质瘤化生性的理解。在动态“元网络”（网络的网络）框架下研究了神经胶质瘤诱导的语言网络重组。在健康对照和神经胶质瘤患者中，静息状态下的框架语言网络动态稳健地聚类为 4 个时间重复状态。在左半球神经胶质瘤患者中观察到空间而非时间语言缺陷 - 严重程度依赖性的 dFC 异常涉及语言网络。语言网络动态显着预测个体患者的语言分数。
Brain network dynamics not only endow the brain with flexible coordination for various cognitive processes but also with a huge potential of neuroplasticity for development, skill learning, and after cerebral injury. Diffusive and progressive glioma infiltration triggers the neuroplasticity for functional compensation, which is an outstanding pathophysiological model for the investigation of network reorganization underlying neuroplasticity. In this study, we employed dynamic conditional correlation to construct framewise language networks and investigated dynamic reorganizations in 83 patients with left hemispheric gliomas involving language networks (40 patients without aphasia and 43 patients with aphasia). We found that, in healthy controls (HCs) and patients, the language network dynamics in resting state clustered into 4 temporal-reoccurring states. Language deficits-severity-dependent topological abnormalities of dFCs were observed. Compared with HCs, suboptimal language network dynamics were observed for those patients without aphasia, while more severe network disruptions were observed for those patients with aphasia. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the 4 states significantly predicted individual patients’ language scores. These findings shed light on our understanding of metaplasticity in glioma.Glioma-induced language network reorganizations were investigated under a dynamic “meta-networking” (network of networks) framework.In healthy controls and patients with glioma, the framewise language network dynamics in resting-state robustly clustered into 4 temporal-reoccurring states.The spatial but not temporal language deficits-severity-dependent abnormalities of dFCs were observed in patients with left hemispheric gliomas involving language network.Language network dynamics significantly predicted individual patients’ language scores.